rRNASelector: A computer program for selecting ribosomal RNA encoding sequences from metagenomic and metatranscriptomic shotgun libraries

Metagenomic and metatranscriptomic shotgun sequencing techniques are gaining popularity as more cost-effective next-generation sequencing technologies become commercially available. The initial stage of bioinfor-matic analysis generally involves the identification of phylogenetic markers such as ribosomal RNA genes. The sequencing reads that do not code for rRNA can then be used for protein-based analysis. Hidden Markov model is a well-known method for pattern recognition. Hidden Markov models that are trained on well-curated rRNA sequence databases have been successfully used to identify DNA sequence coding for rRNAs in pro-karyotes. Here, we introduce rRNASelector, which is a computer program for selecting rRNA genes from massive metagenomic and metatranscriptomic sequences using hidden Markov models. The program successfully identified prokaryotic 5S, 26S, and 23S rRNA genes from Roche 454 FLX Titanium-based metagenomic and metatranscriptomic libraries. The rRNASelector program is available at http://sw.ezbiocloud.net/rrnaselector.

[1]  J. Handelsman,et al.  Cloning the Soil Metagenome: a Strategy for Accessing the Genetic and Functional Diversity of Uncultured Microorganisms , 2000, Applied and Environmental Microbiology.

[2]  Maciej Szymanski,et al.  5S Ribosomal RNA Database , 2002, Nucleic Acids Res..

[3]  O. White,et al.  Environmental Genome Shotgun Sequencing of the Sargasso Sea , 2004, Science.

[4]  U. Göbel,et al.  Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. , 1997, FEMS microbiology reviews.

[5]  Sean R. Eddy,et al.  Profile hidden Markov models , 1998, Bioinform..

[6]  Alexander F. Auch,et al.  MEGAN analysis of metagenomic data. , 2007, Genome research.

[7]  Sean R Eddy,et al.  A new generation of homology search tools based on probabilistic inference. , 2009, Genome informatics. International Conference on Genome Informatics.

[8]  J. Handelsman Metagenomics: Application of Genomics to Uncultured Microorganisms , 2004, Microbiology and Molecular Biology Reviews.

[9]  J. Chun,et al.  EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences. , 2007, International journal of systematic and evolutionary microbiology.

[10]  Andreas Wilke,et al.  phylogenetic and functional analysis of metagenomes , 2022 .

[11]  J. Handelsman,et al.  Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. , 1998, Chemistry & biology.

[12]  James R. Knight,et al.  Genome sequencing in microfabricated high-density picolitre reactors , 2005, Nature.

[13]  Martin F. Polz,et al.  Bias in Template-to-Product Ratios in Multitemplate PCR , 1998, Applied and Environmental Microbiology.

[14]  D. Antonopoulos,et al.  Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. , 2010, Cold Spring Harbor protocols.

[15]  Ying Huang,et al.  Bioinformatics Applications Note Identification of Ribosomal Rna Genes in Metagenomic Fragments , 2022 .

[16]  Peter F. Hallin,et al.  RNAmmer: consistent and rapid annotation of ribosomal RNA genes , 2007, Nucleic acids research.

[17]  E. Delong,et al.  Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing , 1991, Journal of bacteriology.

[18]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[19]  Steven M. Johnson,et al.  A high-resolution, nucleosome position map of C. elegans reveals a lack of universal sequence-dictated positioning. , 2008, Genome research.

[20]  E. Mardis Next-generation DNA sequencing methods. , 2008, Annual review of genomics and human genetics.